Decision Instrument · Digital Health Infrastructure · Payer or Provider
NPHIES Maturity Diagnostic
A 25-question structured assessment evaluating your organisation NPHIES maturity level: from compliance filing through to clinical intelligence. Covers data quality, analytical capability, governance, strategic use cases, and reform readiness.
5 domains
25 questions
Approximately 15 minutes
Payer or provider perspective
Self-assessment version. Available as a facilitated Full Diagnostic with independent data quality review, analytical capability gap analysis, and a board-ready NPHIES maturity roadmap.
Several questions have payer-specific and provider-specific variants. Select your perspective before starting.
Section 1 of 50% complete
Section 1 of 5
Data Quality & Completeness
Evaluates whether the data your organisation submits to and receives from NPHIES is complete and specific enough to support analytics: not just compliant for claims processing.
Tests against: Garbage In / Garbage Out · Coding Inconsistency
Question 1
Does your organisation actively monitor NPHIES data completeness: tracking the proportion of submitted records with fully populated Minimum Data Set fields versus those with missing or default entries?
Strong - 3 pointsData completeness is monitored monthly with defined thresholds. Records with missing or default fields are flagged before submission and completeness rates are reported as a performance metric.
Partial - 2 pointsData completeness is understood broadly but is not systematically monitored at field level or reported as a performance metric.
Weak - 0 pointsNo active monitoring of data completeness. NPHIES submissions are validated for technical compliance but field-level completeness is not tracked.
Question 2
Is your ICD-10-AM/ACHI/ACS coding sufficiently specific to support clinical analytics: using four and five-character codes that enable meaningful diagnosis pattern analysis rather than minimum-compliance three-character entries?
Strong - 3 pointsCoding specificity is actively managed and benchmarked. Coders are trained for specificity, not minimum compliance. The proportion of full-specificity codes is tracked.
Partial - 2 pointsCoding meets NPHIES compliance requirements but specificity is not actively managed or benchmarked.
Weak - 0 pointsCoding is at minimum compliance level. Specificity beyond what is required for claim acceptance is not a managed standard.
Question 3
Are NPHIES denial codes and rejection reasons systematically captured, categorised, and fed back into coding and documentation improvement processes: rather than treated only as individual claim exceptions requiring resubmission?
Strong - 3 pointsDenial codes are categorised monthly, trends drive structured improvement programmes, and the impact on denial rates is measured.
Partial - 2 pointsDenials are tracked for resubmission but pattern analysis for systemic improvement is not consistently performed.
Weak - 0 pointsDenials are handled case by case with no systematic pattern analysis.
Question 4
Do you conduct regular data quality audits of NPHIES submissions received from network providers: identifying providers whose coding patterns or data completeness fall below analytical standards?
Do you conduct regular internal data quality audits comparing NPHIES submission records against the underlying clinical documentation: verifying codes submitted accurately reflect the clinical record?
Strong - 3 pointsQuarterly data quality audits with defined scope, documented findings, and a structured remediation process tracking improvement across audit cycles.
Partial - 2 pointsAudits occur but are infrequent, limited in scope, or not linked to systematic remediation.
Weak - 0 pointsNo formal data quality audit programme for NPHIES submissions.
Question 5
Is NPHIES drug coding (SFDA codes, NPHIES item codes) sufficiently complete and accurate to support pharmaceutical analytics including prescribing patterns, generic substitution rates, and formulary adherence?
Strong - 3 pointsDrug coding completeness and SFDA code matching rates are actively monitored and meet the standard required for pharmaceutical analytics and formulary management decisions.
Partial - 2 pointsDrug coding meets compliance requirements but completeness and matching rates have not been assessed for analytical sufficiency.
Weak - 0 pointsDrug coding quality is not actively managed beyond compliance.
Section 2 of 5
Analytical Capability
Tests whether your organisation has the technical infrastructure, analytical talent, and data architecture to interrogate NPHIES transaction data for strategic intelligence: not just operational reporting.
Tests against: Analytical Infrastructure Gap · Reporting vs Intelligence
Question 6
Does your organisation have direct, structured access to NPHIES transaction history in an analytical environment: enabling queries across the full transaction universe without dependency on manual export or IT support?
Strong - 3 pointsNPHIES transaction data is accessible in an internal data warehouse or similar environment where analysts can run queries across full transaction history independently.
Partial - 2 pointsNPHIES data is available for analysis but requires periodic export, IT mediation, or is accessible only in summarised form.
Weak - 0 pointsNo structured analytical access. Analysis relies on dashboard views or manual reports from the NPHIES portal.
Question 7
Does your analytical team have clinical coding competency to interpret NPHIES diagnosis and procedure patterns: distinguishing between coding artefacts, documentation patterns, and genuine clinical utilisation signals?
Strong - 3 pointsAt least one analyst with certified ICD-10-AM/ACHI/ACS competency is embedded in or regularly available to the analytics function.
Partial - 2 pointsSome clinical coding knowledge exists but is not formalised or systematically applied to NPHIES data interpretation.
Weak - 0 pointsNo clinical coding competency in the analytics function.
Question 8
Can your analytics function produce on demand a case-mix analysis of your beneficiary population showing DRG weight distribution, high-cost diagnosis concentrations, and utilisation outliers by provider and specialty?
Can your analytics function produce on demand a case-mix analysis of admitted patients showing DRG weight distribution, service line contribution, and length-of-stay patterns benchmarked against NPHIES network norms?
Strong - 3 pointsOn-demand case-mix analysis is available from NPHIES data, updated at least monthly, and used in strategy and contract decisions.
Partial - 2 pointsCase-mix analysis can be produced but requires significant manual effort, is infrequent, or lacks DRG and network benchmarking.
Does your organisation use NPHIES pre-authorisation patterns as a leading indicator: monitoring approval and denial trends by procedure, diagnosis, and provider to anticipate future claims exposure or clinical practice concerns?
Strong - 3 pointsPre-authorisation patterns are monitored as a leading indicator and feed into clinical governance and underwriting decisions on a regular cycle.
Partial - 2 pointsPre-authorisation data is used for operational management but not systematically as a clinical or financial leading indicator.
Weak - 0 pointsPre-authorisation data is used only for individual case decisions. No aggregate pattern analysis is conducted.
Question 10
Has your organisation used NPHIES longitudinal data across multiple periods to track patient-level utilisation trajectories: identifying cohorts with escalating risk or emerging chronic disease burden before acute events occur?
Strong - 3 pointsLongitudinal patient-level analysis from NPHIES data produces risk stratification used for targeted intervention before acute utilisation events occur.
Partial - 2 pointsSome longitudinal analysis has been attempted but is not systematic or integrated into clinical or underwriting decisions.
Weak - 0 pointsNo longitudinal NPHIES analysis. Patient risk is assessed at point of contact or claims review only.
Section 3 of 5
Governance Architecture
Evaluates whether NPHIES data governance ensures data quality, defines analytical accountability, and produces findings that decision-makers can act on with confidence.
Tests against: Unactionable Insight · Data Without Accountability
Question 11
Is there a named data owner or governance lead for NPHIES data: accountable for data quality standards, analytical output validity, and translation of NPHIES findings into board-level decision inputs?
Strong - 3 pointsA named senior data owner holds accountability for NPHIES data governance with defined authority over quality standards, analytical methodology, and leadership escalation.
Partial - 2 pointsResponsibility is distributed across IT, analytics, and operations without a single named accountability point.
Weak - 0 pointsNo named data governance accountability for NPHIES.
Question 12
Does the board or a board-level committee receive regular reporting derived from NPHIES analytics: covering utilisation patterns, denial trends, case-mix intelligence, or population risk indicators as inputs to strategy decisions?
Strong - 3 pointsQuarterly board reporting includes NPHIES-derived analytics covering at least two intelligence categories connected to explicit strategy decisions.
Partial - 2 pointsNPHIES data reaches leadership occasionally but not in a structured cycle or connected to explicit strategy decisions.
Weak - 0 pointsNPHIES analytics do not reach board level.
Question 13
Are there documented data sharing protocols for NPHIES-derived analytics: defining who can access what level of patient-level data, under what conditions, and with what audit trail?
Strong - 3 pointsDocumented access protocols exist with role-based access controls, audit logging, and a defined review process for requests outside normal parameters.
Partial - 2 pointsSome access controls exist but are not formally documented or consistently enforced.
Weak - 0 pointsNo formal data access protocols for NPHIES data.
Question 14
Is there a defined process for translating NPHIES analytical findings into organisational decisions: with named decision owners, defined response timelines, and tracking of whether findings led to measurable change?
Strong - 3 pointsA structured insight-to-action process exists with named owners, tracked timelines, and measurement of whether NPHIES findings produce measurable outcomes.
Partial - 2 pointsFindings are shared with decision-makers but translation to action is informal without defined ownership or outcome tracking.
Weak - 0 pointsNo structured process for translating NPHIES findings into decisions.
Question 15
Has your organisation assessed its NPHIES data architecture for AI readiness: evaluating whether data volume, quality, and labelling standards meet the requirements for machine learning applications at the transaction level?
Strong - 3 pointsAn AI readiness assessment for NPHIES data has been completed covering volume, quality, and labelling. Gaps are identified and a remediation roadmap exists.
Partial - 2 pointsAI use cases are being considered but a formal data readiness assessment has not been completed.
Weak - 0 pointsAI applications for NPHIES data have not been assessed.
Section 4 of 5
Intelligence Use Cases
Tests whether NPHIES data is actively informing the strategic decisions that matter most: DRG case-mix management, population health, FWA detection, and network performance benchmarking.
Tests against: Compliance Level Plateau · Missed Strategic Value
Question 16
Has NPHIES data been used to reprice actuarial assumptions for your health book: replacing FFS utilisation history with DRG cost weight analysis specific to your beneficiary population and provider network?
Has NPHIES DRG data been used to model revenue impact under the AR-DRG reimbursement transition: comparing current case-mix weights against projected bundled payment rates by service line?
Strong - 3 pointsNPHIES data has been used for DRG-based repricing or revenue modelling with results integrated into underwriting or financial planning for the transition period.
Partial - 2 pointsDRG data analysis has been conducted but not yet integrated into pricing or financial planning decisions.
Weak - 0 pointsNo DRG-based repricing or revenue modelling from NPHIES data.
Question 17
Has NPHIES transaction data been used to identify high-risk patient cohorts for preventive intervention: implementing a population health programme targeting beneficiaries whose utilisation trajectory indicates escalating chronic disease burden?
Strong - 3 pointsA population health programme driven by NPHIES risk stratification is operational with defined cohorts, active interventions, and outcomes tracked against the NPHIES baseline.
Partial - 2 pointsPopulation health is a stated ambition and some NPHIES analysis has been done, but an operational intervention programme does not yet exist.
Weak - 0 pointsNo population health programme using NPHIES data.
Question 18
Is NPHIES data feeding your FWA detection system: enabling network-level outlier pattern analysis (payer) or internal coding benchmark monitoring (provider) beyond individual claim review?
Strong - 3 pointsNPHIES transaction data is a primary input to FWA detection with automated alerts for defined deviation thresholds at network or internal level.
Partial - 2 pointsNPHIES data is used in FWA review but not in a systematic pattern detection system with automated alerting.
Weak - 0 pointsFWA detection does not draw on NPHIES pattern analytics.
Question 19
Is NPHIES data used to benchmark network or internal performance against market norms: denial rates by procedure, length of stay by DRG, prescribing patterns by specialty, generating actionable intelligence for network or clinical governance?
Strong - 3 pointsMarket benchmarking from NPHIES data is a regular input to network management or clinical governance. Outlier performance triggers structured review and improvement protocols.
Partial - 2 pointsSome benchmarking is conducted but is infrequent or does not trigger consistent structured review.
Has NPHIES data been used to inform VBHC contract design: providing the outcome measurement baseline, utilisation benchmark, and cost weight reference point that makes value-based payment terms analytically defensible?
Strong - 3 pointsNPHIES data has been used in VBHC contract design or evaluation: providing outcome baselines, utilisation benchmarks, or DRG cost weights that underpin payment terms.
Partial - 2 pointsNPHIES data has been considered in VBHC discussions but not formally integrated into payment term design.
Weak - 0 pointsVBHC contract terms are not informed by NPHIES data.
Section 5 of 5
Reform Readiness
Evaluates whether your NPHIES maturity is positioned to support the analytical demands of the Saudi healthcare reform through 2027 and 2028: AR-DRG transition, NISS expansion, RBC framework, and IA regulatory requirements.
Tests against: Compliance Capability in an Intelligence-Demand Environment
Question 21
Has your organisation used NPHIES data to model the utilisation and financial impact of the NISS beneficiary expansion: projecting how adding dependants will shift case-mix and cost distribution based on existing population data?
Strong - 3 pointsA NISS expansion impact model has been built from NPHIES data with quantified projections for case-mix shift, utilisation change, and financial exposure.
Partial - 2 pointsNISS impact has been considered but a NPHIES-data-driven model with quantified projections has not been produced.
Is your NPHIES data architecture configured to track AR-DRG weight distribution over time: enabling detection of case-mix drift, DRG upcoding patterns, and reimbursement model performance as the FFS to bundled episode transition progresses?
Strong - 3 pointsDRG weight distribution tracking is operational with defined baseline and alerting for significant drift connected to reimbursement performance monitoring.
Partial - 2 pointsDRG data is available but time-series tracking and drift detection are not yet configured as operational monitoring tools.
Weak - 0 pointsNo DRG weight tracking architecture in place.
Question 23
Has NPHIES data been used to generate the risk profile analysis required for the Insurance Authority RBC framework: providing the actuarial evidence base for capital adequacy calculations grounded in actual claims experience?
Strong - 3 pointsNPHIES transaction data is a primary input to RBC risk profile analysis. Capital adequacy calculations are grounded in actual claims history from the NPHIES transaction record.
Partial - 2 pointsSome NPHIES data has been used in RBC preparation but capital adequacy is not fully grounded in NPHIES-derived claims experience.
Weak - 0 pointsRBC calculations do not draw on NPHIES transaction data.
Question 24
Is your NPHIES maturity roadmap formally documented: with defined maturity targets, investment requirements, timelines linked to reform milestones, and board-approved resource allocation?
Strong - 3 pointsA formal board-approved NPHIES maturity roadmap exists with defined targets, investment budget, timelines linked to AR-DRG and NISS milestones, and named executive accountability.
Partial - 2 pointsA maturity roadmap exists at a high level but lacks board approval, specific investment allocation, or reform-linked timelines.
Weak - 0 pointsNo NPHIES maturity roadmap exists.
Question 25
Is your organisation engaged with the CHI and IA roadmap for NPHIES intelligence use cases: including the beneficiary health management programme, AI-assisted monitoring, and quality-based healthcare models referenced in official communications?
Strong - 3 pointsThe regulatory NPHIES intelligence roadmap is actively monitored and your internal maturity plan is explicitly aligned to it with initiatives mapped to CHI programme milestones.
Partial - 2 pointsThe regulatory roadmap is known but internal planning has not been formally aligned to it.
Weak - 0 pointsThe CHI and IA NPHIES intelligence roadmap is not being tracked or acted on.
NPHIES Maturity Diagnostic - Results
Your NPHIES Maturity Profile
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Domain Breakdown
Data Quality
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Analytical Capability
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Governance Architecture
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Intelligence Use Cases
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Reform Readiness
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Indicative Findings
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This self-assessment shows the methodology. The facilitated diagnostic goes further.
A facilitated HealthElevate NPHIES diagnostic includes independent data quality review, analytical capability gap analysis, and a board-ready maturity roadmap with sequenced investment recommendations linked to reform milestones.
NPHIES: From Compliance Infrastructure to Clinical Intelligence Asset
The analytical context behind this instrument: what 130 million transactions represent, the four maturity levels, and why the reform period makes the transition urgent.